Tag: learning resources

It’s hard to believe a whole month of 2019 is already gone. A new year is a great time to learn new things. In that spirit, I would like to share with you several resources I love for learning marketing analytics. As many of you are aware, marketing analytics has become an important skill set to have in today’s workplace. This list will help put you on the right path to becoming more proficient in marketing analytics.

Online Courses

1. Marketing Analytics Course by Columbia University on EdX. As part of the ColumbiaX’s MicroMaster’s program in business analytics, this 12-week online course offers a great introduction to marketing analytics. It covers key topics such as customer segmentation, customer choice modeling, optimal pricing, and conjoint analysis for new product design. Each week you watch a set of pre-recorded videos and complete an assignment on a given topic. You can also ask instructors questions on the discussion board. I like that the course teaches the basic concepts of marketing analytics as well as their implementation using R. You can take the course for free, or you can pay to get a course completion certificate at the end. It appears to be offered several times a year. The most recent one started on Jan. 27, 2019, not too late to sign on if you are interested.

2. Marketing Analytics courses from DataCamp. The DataCamp website is devoted to data science training. It offers 200+ data analytic courses and uses a subscription-based model. Currently I can find four specific marketing analytic courses: Marketing Analytics in R: Statistical Modeling; Marketing Analytics in R: Choice Modeling; Cluster Analysis in R; and Sentiment Analysis in R. There are also many other general data analytics courses dealing with either tools (R, Python) or methods (e.g., machine learning, data visualization, statistics, etc.). Each of these courses is not very long, at a few hours. But across all courses, there are a lot of things to learn.

3. Social Media Data Analytics. For those interested in analyzing textual data rather than numbers, there are a number of online courses available on this topic. I chose this Coursera course for its wider coverage and depth of materials. Offered by my alma mater, Rutgers University, this four-week course covers the basics of extracting and analyzing large quantities of textual data from social media. The main tools taught are Python and R. It covers a limited number of social media channels, including Twitter, YouTube, Flickr, and Yelp. But the working knowledge of APIs acquired through the course should readily extend to other social media channels.

Books

4. Freakonomics. This is a book you probably don’t expect to see on this list. It doesn’t teach any analytical methods, but it is a great demonstration of how we can find answers to questions through smart and creative use of data. I strongly believe that the abilities to ask interesting questions and translating numbers into insights are just as important as crunching numbers and running sophisticated statistical models. This book is a good example of the data to insight process.

5. Quantitative Models in Marketing Research. Compared with freakonomics, this book represents the other end of the spectrum and focuses more on crunching numbers (i.e., data modeling). This concise book is organized by data types (e.g., continuous variables, binary variables, etc.) and describes the most common ways of modeling each data type. Each topic is organized similarly, in the order of general model description, estimation, diagnostics, an application example, followed by more advanced issues on the topic. I like that each chapter comes with an illustrative example, and you can download the data used in each example to try the analysis yourself. For some topics, the book offers the analysis code in Eviews, another common statistical package commonly used in economics. The book does requires knowledge of basic statistics and matrix algebra to digest more easily.

6. R for Marketing Research and Analytics. This book shows you how to use the open-source software R to tackle the most common marketing analytics problems, such as segmentation, basket analysis, and data summary and visualization. It does assume some familiarity with the underlying concepts and focuses mostly on their implementation in R. I like that the book offers clear R script examples in each area, making it a very hands-on learning experience.

7. Web Analytics 2.0. Published in 2009, some might consider this book a dinosaur in the digital age. But I love this book and think it is as relevant as ever. Yes, the specific examples using the various web analytics tools may be outdated. But the basic approach behind how to identify and interpret key web metrics is quite universal and timeless. I like the book’s very interpretation-oriented approach, focusing on what the different metrics mean or sometimes do not mean. This is a great starter book if you want to become familiar with the world of web analytics.

Websites

8. Kaggle.com. Devoted to data science, Kaggle.com offers a variety of resources for current and future data scientists. It has over 10,000 datasets on a wide variety of topics, to give you hands-on experience with a dataset of your interest. You can take free short online courses on important data science tools such as R, Python, SQL, and machine learning. If you feel good about your data analytic skills, you can even participate in various Kaggle competitions. At the time of writing, there are 16 active competitions running with prizes ranging from knowledge and swag to $100,000.

9. Occam’s Razor blog. Blog from the author of the Web Analytics book mentioned earlier, Avinash Kaushik. Just like the book, the author delivers sensible advice on how to do analytics right, with a razor-sharp focus on identifying what is important. It is clear from this blog that more is not always better when it comes to marketing analytics.

10. Big Data, Plainly Spoken blog. This blog by bestselling author Kaiser Fung discusses the use and interpretation of data in diverse domains, including economics, education, and politics. What makes the blog stand out is its making sense of data in layman’s words. Just like the Freakonomics book, you can learn a lot about translating data into meaningful information here.

 
I hope you find the list helpful. If you are aware of a good learning resource that is not listed here, please feel free to add a comment below or drop me a note. Happy learning!